In the pharmaceutical industry, model-based prediction is a crucial stage in process development that allows pharmaceutical companies to simulate different scenarios toward improving process efficiency, reducing costs, and enhancing product quality. Nevertheless, ensuring the quality of formulated pharmaceutical products through the management of raw material variations has always been a challenging task. In this work, data-driven chance-constrained recurrent neural networks (CCRNNs) are developed to address the issue arising from raw material uncertainty. Our goal is to explore how, by proactively incorporating uncertainty into the model training process, more accurate predictions and enhanced robustness can be realized. The proposed approach is tested on a fluid bed dryer (FBD) from a continuous pharmaceutical manufacturing pilot plant. The results demonstrate that CCRNN models offer more robust and accurate predictions for the critical quality attribute (CQA)—in this case, moisture content—when material variations occur, compared with conventional recurrent neural network-based models.